wget https://download.elastic.co/elasticsearch/release/org/elasticsearch/distribution/tar/elasticsearch/2.3.3/elasticsearch-2.3.3.tar.gz
集群安装：
三个节点：master,slave1,slvae2
vi elasticsearch.yml
cluster.name: my-application
node.name: node-3(节点独有的名称，注意唯一性)
network.host: 192.168.137.117
http.port: 9200
discovery.zen.ping.unicast.hosts: ["master","slave1", "slave2"]
安装插件
/home/qun/soft/elasticsearch-2.3.3/bin/plugin install analysis-icu
/home/qun/soft/elasticsearch-2.3.3/bin/plugin install mobz/elasticsearch-head
marvel：
/home/qun/soft/elasticsearch-2.3.3/bin/plugin install license
/home/qun/soft/elasticsearch-2.3.3/bin/plugin install marvel-agent
在各个节点上执行：
elasticsearch -d
杀死节点
kill -9 `ps -ef|grep elasticsearch|awk ‘{print $2}‘`
启动
/home/qun/soft/elasticsearch-2.3.3/bin/elasticsearch -d
访问集群：
http://master:9200/_plugin/head/
一个节点(node)就是一个Elasticsearch实例，而一个集群(cluster)由一个或多个节点组成，它们具有相同的cluster.name，
它们协同工作，分享数据和负载。当加入新的节点或者删除一个节点时，集群就会感知到并平衡数据。
做为用户，我们能够与集群中的任何节点通信，包括主节点。每一个节点都知道文档存在于哪个节点上，它们可以转发请求到相应的节点上。
我们访问的节点负责收集各节点返回的数据，最后一起返回给客户端。这一切都由Elasticsearch处理。
获取集群状态
http://master:9200/_cluster/health/
{
"cluster_name": "my-application",
"status": "green",
"timed_out": false,
"number_of_nodes": 3,
"number_of_data_nodes": 3,
"active_primary_shards": 22,
"active_shards": 44,
"relocating_shards": 0,
"initializing_shards": 0,
"unassigned_shards": 0,
"delayed_unassigned_shards": 0,
"number_of_pending_tasks": 0,
"number_of_in_flight_fetch": 0,
"task_max_waiting_in_queue_millis": 0,
"active_shards_percent_as_number": 100
}设置添加分片
PUT /blogs/_settings
{
"number_of_replicas" : 2
}
删除索引
curl -XDELETE ‘http://master:9200/.marvel-es-1-2016.05.29‘
curl -XDELETE ‘http://master:9200/.marvel-es-data-1‘
安装IK分词器(https://github.com/medcl/elasticsearch-analysis-ik)
wget https://github.com/medcl/elasticsearch-analysis-ik/archive/master.zip
mvn package
mkdir -p /home/qun/soft/elasticsearch-2.3.4/plugins/ik
cp /home/qun/soft/elasticsearch-2.3.3/elasticsearch-analysis-ik-master/target/releases/elasticsearch-analysis-ik-1.9.3.zip /home/qun/soft/elasticsearch-2.3.3/plugins/ik
unzip elasticsearch-analysis-ik-1.9.3.zip
测试分词
/twitter/_analyze?analyzer=standard&pretty=true&text=我是中国人
/twitter/_analyze?analyzer=ik&pretty=true&text=我是中国人
添加用户自定义词典：
elasticsearch-2.3.3/plugins/ik/config/IKAnalyzer.cfg.xml
栗子:添加sougou.dic,分号分隔，相对路径，重启es集群
<entry key="ext_dict">custom/mydict.dic;custom/single_word_low_freq.dic;custom/sougou.dic</entry>
<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE properties SYSTEM "http://java.sun.com/dtd/properties.dtd"> <properties> <comment>IK Analyzer 扩展配置</comment> <!--用户可以在这里配置自己的扩展字典 --> <entry key="ext_dict">custom/mydict.dic;custom/single_word_low_freq.dic;custom/sougou.dic</entry> <!--用户可以在这里配置自己的扩展停止词字典--> <entry key="ext_stopwords">custom/ext_stopword.dic</entry> <!--用户可以在这里配置远程扩展字典 --> <!-- <entry key="remote_ext_dict">words_location</entry> --> <!--用户可以在这里配置远程扩展停止词字典--> <!-- <entry key="remote_ext_stopwords">words_location</entry> --> </properties>
配置同义词
修改：elasticsearch-2.3.3/config/elasticsearch.yml,在末尾加上如下内容
index: analysis: analyzer: ik_syno: type: custom tokenizer: ik_max_word filter: [my_synonym_filter] ik_syno_smart: type: custom tokenizer: ik_smart filter: [my_synonym_filter] filter: my_synonym_filter: type: synonym synonyms_path: analysis/synonym.txt
添加词典：
mkdir -p elasticsearch-2.3.3/config/analysis
vi elasticsearch-2.3.3/config/analysis/synonym.txt
ipod, i-pod, i pod
foozball , foosball
universe , cosmos
西红柿, 番茄
马铃薯, 土豆
测试同义词：
GET /iktest/_analyze?analyzer=ik_syno_smart&pretty=true&text=马铃薯西红柿
结果：
{
"tokens": [
{
"token": "马铃薯",
"start_offset": 0,
"end_offset": 3,
"type": "CN_WORD",
"position": 0
}
,
{
"token": "土豆",
"start_offset": 0,
"end_offset": 3,
"type": "SYNONYM",
"position": 0
}
,
{
"token": "西红柿",
"start_offset": 3,
"end_offset": 6,
"type": "CN_WORD",
"position": 1
}
,
{
"token": "番茄",
"start_offset": 3,
"end_offset": 6,
"type": "SYNONYM",
"position": 1
}
]
}原文地址：http://11670039.blog.51cto.com/11660039/1825728
